Split HadoopRDD into one for general Hadoop datasets and one tailored to Hadoop files, which is a common case.

This is the first step to avoiding unnecessary Configuration broadcasts per HadoopRDD instantiation.
Add a cache for HadoopRDD metadata needed during computation.

Currently, the cache is in SparkHadoopUtils, since it's conveniently a member of the SparkEnv.
Move Configuration broadcasts to SparkContext.
Merge remote-tracking branch 'oldsparkme/hadoopRDD-broadcast-change' into hadoop-config-cache
Merge HadoopDatasetRDD into HadoopRDD.
Fix API changes; lines > 100 chars.
Make HadoopRDD object Spark private.
Some comments regarding JobConf and InputFormat caching for HadoopRDDs.
Merge pull request #20 from harveyfeng/hadoop-config-cache

Allow users to pass broadcasted Configurations and cache InputFormats across Hadoop file reads.

Note: originally from https://github.com/mesos/spark/pull/942

Currently motivated by Shark queries on Hive-partitioned tables, where there's a JobConf broadcast for every Hive-partition (i.e., every subdirectory read). The only thing different about those JobConfs is the input path - the Hadoop Configuration that the JobConfs are constructed from remain the same.
This PR only modifies the old Hadoop API RDDs, but similar additions to the new API might reduce computation latencies a little bit for high-frequency FileInputDStreams (which only uses the new API right now).

As a small bonus, added InputFormats caching, to avoid reflection calls for every RDD#compute().

Few other notes:

Added a general soft-reference hashmap in SparkHadoopUtil because I wanted to avoid adding another class to SparkEnv.
SparkContext default hadoopConfiguration isn't cached. There's no equals() method for Configuration, so there isn't a good way to determine when configuration properties have changed.